Re-identifying individuals in images can be a difficult task, because many images are not taken with sufficiently high resolution to use facial recognition software. Conventional methods of re-identification depend on a comparison of a first total image to a second total image. Comparing the two total images, however, requires compressing image data for each image by one or more orders of magnitude, resulting in a significant loss of data and resolution. As a result, conventional methods are error prone and may return false negatives due to, among other things, differing conditions between the images being compared, such as different lighting and a change in pose of the individual.